Predictive Value of Polypharmacy and Inappropriate Medication Use in Older Emergency Department Patients: A Retrospective Observational Study

Predictive Value of Polypharmacy and Inappropriate Medication Use in Older Emergency Department Patients: A Retrospective Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Value of Polypharmacy and Inappropriate Medication Use in Older Emergency Department Patients: A Retrospective Observational Study Ria Holstein, Mari Hongisto, Esa Jämsen, Eeva Saario, Kirsi Kvarnström, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9326583/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background. Polypharmacy and potentially inappropriate medication (PIM) use are well-known risk factors for drug-related problems among older adults, but their association with clinical outcomes of older emergency department (ED) patients remains unclear. This study investigated the predictive value of polypharmacy and PIM use in older ED patient population. Methods. We included 392 ED patients aged ≥ 75 years. Polypharmacy and PIM (according to 2019 Beers criteria) use were determined with binomial variables (polypharmacy ≥ 5 regular medications, excessive polypharmacy ≥ 10 medications, PIM use ≥ 1 PIMs) and ordinal variables (the number of regular medications and PIMs). Study outcomes included mortality, hospital admission following index ED presentation, and ED revisit within 90 days. Statistically, we used logistic regression model adjusted for age, sex, renal function and the Charlson Comorbidity Index (CCI). Results. Polypharmacy was present in 80% and excessive polypharmacy in 30% of patients. As a binomial variable, polypharmacy did not predict any outcomes, but excessive polypharmacy [adjusted OR 1.35 (95% CI 1.12–4.93), p = 0.024] and increasing number of medications [1.09 (1.03–1.16), p = 0.014] predicted a higher risk of ED revisits. Polypharmacy [OR 0.17 (0.06–0.45), p < 0.001], excessive polypharmacy [0.21 (0.06–0.70), p = 0.01] and increasing number of medications [0.83 (0.72–0.94, p = 0.005] were associated with lower mortality risk. Conclusions. Polypharmacy or PIM use are not associated with higher mortality or hospital admission risk. Polypharmacy predicts a higher risk of ED revisits, but only with the threshold of ≥ 10 medications. Screening polypharmacy with novel numerical thresholds could be useful in detecting older ED patients at high risk of ED revisits. Emergency medicine geriatrics geriatric emergency medicine polypharmacy potentially inappropriate medication drug-related problems drug-related emergency department visit frailty screening tool Figures Figure 1 Background Drug-related problems (DRP), such as adverse drug events (ADE), are estimated to account for over one-fifth of all emergency department (ED) visits ( 1 ). Despite their high prevalence, a significant proportion of drug-related ED visits remain unrecognized, increasing the risks of misdiagnosis and ED readmission ( 2 – 4 ). Older ED patients are at a high risk of drug-related ED visits ( 5 – 10 ). This vulnerability is largely attributed to polypharmacy and potentially inappropriate medication (PIM) use, both of which are common in this population and are well-established risk factors for DRPs, increased healthcare use and hospitalizations ( 11 , 12 ). Although there is variance among definitions, previous literature has classically defined polypharmacy as the use of five or more regular medications, and excessive polypharmacy as the use of ten or more regular medications ( 11 , 13 ). PIMs, again, are medications in which the potential risks outweigh the expected benefits among older individuals. PIMs are often defined with explicit criteria, of which the most common are the American Beers or the European Screening Tool of Older Persons' Prescriptions / Screening Tool to Alert to Right Treatment (STOPP/START) criteria ( 14 , 15 ). With growing recognition of the unique needs of older adults, polypharmacy and PIM screening have become crucial components in the medication reviews of comprehensive geriatric assessment (CGA) in both outpatient and inpatient settings ( 16 , 17 ). Consequently, geriatric acute care guidelines developed by the American College of Emergency Physicians (ACEP), the European Society of Emergency Medicine (EUSEM) and the European Geriatric Medicine Society (EuGMS) have recommended that polypharmacy and PIM screening as a part of geriatric assessments in ED setting ( 18 , 19 ). Furthermore, polypharmacy screening is included as an item in several ED-based frailty screening tools, such as Identification of Seniors at Risk (ISAR) and the Triage Risk Screening Tool (TRST) ( 20 , 21 ). However, evidence of the predictive value of polypharmacy and PIM use on short-term adverse outcomes as well as of the benefits of their screening in ED setting remains limited. To our knowledge, only one study has evaluated polypharmacy as a predictor of short-term adverse outcomes among older ED patients while adjusting for underlying comorbidities, and no comparable studies have been conducted regarding PIM use ( 22 ). Thus, the aim of this study was to determine the predictive value of polypharmacy and PIM use for adverse outcomes among older ED patients. We hypothesized that both polypharmacy and PIM use would be associated with increased risks of mortality, hospital admissions following the index ED visit, and ED revisits. Methods Study design, setting and population. This retrospective, single center observational study was conducted in Espoo, Finland (population of approx. 300,000). The study population was derived from a previously established cohort of an emergency medical services (EMS) risk screening study, which included community-dwelling adults aged ≥ 70 years transported to the ED by EMS between 10th November 2018 and 30th July 2019 ( 23 ). For this study, a subpopulation of adults aged ≥ 75 years was included. Nine patients were excluded due to multiple ED visits during the study period or missing health records from the index ED visit. All data was collected retrospectively from electronic health records. Variables . For each included patient, the medications used at the time of the index ED visit were recorded from the most recent medication lists. Medications were defined as medicinal substances regularly administered to treat or prevent disease via oral, transdermal, subcutaneous, or inhalation routes. Regularly used supplements and vitamins were also included. Medications administered or prescribed during the index ED visit were excluded. Medication inclusion criteria were established in consultation with a clinical pharmacist. Polypharmacy and PIM use were measured with both binomial and ordinal variables. Polypharmacy was defined as the regular use of ≥ 5 medications, and excessive polypharmacy as ≥ 10 ( 13 ). Additionally, the total number of regularly used medications was determined. Respectively, PIM use was defined as the use of at least one PIM and as the total number of PIMs. PIMs were defined according to the 2019 Beers criteria ( 24 ). To measure the burden of comorbidities, age-adjusted Charlson Comorbidity Index (CCI) was calculated for each patient. CCI ranges from a minimum of 3 to a maximum of 37 ( 25 ). Furthermore, estimated glomerular filtration rates (eGFR), based on the most recently measured serum creatinine and CKD-EPI equation, were calculated. The primary diagnoses and complaints of the ED visits were classified according to the 10th revision of International Statistical Classification of Diseases and Related Health Problems (ICD-10). All data was derived from the electronic health care records of the included patients. Outcomes. The primary study outcomes were mortality, hospital admissions following index ED presentation and ED revisits during the follow-up. The follow-up time was 90 days. Revisits were defined as any ED visit occurring within the follow-up at any of the EDs of the Helsinki University Hospital (HUH). The revisits were identified from electronic hospital databases and confirmed from medical charts. The total number of ED revisits during the follow-up was used as a secondary outcome. Statistical Analysis. Categorical variables are reported as counts (N) and percentages (%), parametric continuous variables as means and standard deviations (SD) and non-parametric continuous variables as medians and interquartile ranges (IQR). Shapiro-Wilk test was used to test for normality of continuous variables. For the comparison between continuous variables, Mann-Whitney U test was used. Logistic regression models were used to assess associations between study variables and primary outcomes. Adjusted models included age, sex, CCI and renal function (eGFR) as confounding factors. The results are presented as odds ratios (OR) with 95% confidence intervals (CI). Associations between study variables and the number of ED revisits were analyzed using Spearman rank correlation. A p-value < 0.05 was considered statistically significant. The statistical analyses were performed with RStudio and IBM SPSS Statistics (versions 28 and 29). Graphical illustrations were generated with GraphPad Prism (v9.0.0.121). Supplementary analysis. Finally, separate analyses and logistic regression models were analyzed using the STOPP/START ( 14 ) and the Finnish Meds75+ ( 26 ) as PIM criteria instead of the Beers criteria. Results A total of 392 older adults with a median age of 84 years (IQR 79–89) were included. Their baseline data are presented in Table 1 . Two-thirds of the patients were female. The median CCI was 6 (IQR 5–7), with a maximum of 12. Nonspecific clinical signs or complaints (ICD-10 R00-R99) accounted for 36% (n = 141) of all ED diagnoses, and traumatic diagnoses (ICD-10 S00-T98) for 27% (n = 106). The most common single diagnosis was malaise and fatigue (ICD-10 R53), accounting for 13% of all diagnoses. One-fifth of the visits (n = 77) were related to falls. Polypharmacy was present in 80% of patients, and excessive polypharmacy in 30%. The median number of regular medications was 7 ( 5 – 10 ), ranging from 0 to 20. A total of 234 (60%) patients used at least one PIM, with a maximum of 7 PIMs. During the 90-day follow-up, 32 patients (8.2%) died, 269 (69%) were admitted to hospital after the index ED visit, and 165 (42%) revisited the ED at least once after the index ED visit. Of the patients with ED revisits, 71 (43%) visited the ED twice or more. Table 1 Baseline characteristics of the included patients (N = 392). Variable N (%) or median (IQR) Age, years, median (IQR) 84 (79–89) Female sex, n (%) 263 (67) CCI, median (IQR) 6 ( 5 – 7 ) Dementia, n (%) 83 ( 21 ) eGFR, ml/min, median (IQR) 61 (43–79) Number of regular medications, median (IQR) 7 ( 5 – 10 ) Polypharmacy, n (%) 315 (80) Excessive polypharmacy, n (%) 117 ( 30 ) Use of at least one PIM, n (%) 234 (60) Number of PIMs, median (IQR) 1 (0–1) Characteristics of the ED visits Most common ED diagnoses (ICD-10) • R53 Malaise and fatigue 50 (12.8) • S01 Open wound of head 22 (5.6) • I48 Atrial fibrillation and flutter 18 (4.6) • R42 Dizziness and giddiness 18 (4.6) • J18 Pneumonia 16 (4.1) ED visit involving a fall, N (%) 77 ( 20 ) IQR = interquartile range, CCI = Charlson Comorbidity Index, eGFR = estimated glomerular filtration rate, PIM = potentially inappropriate medication, ED = emergency department, ICD-10 = International Classification of Diseases, Tenth Revision Logistic regression. The unadjusted and adjusted associations between binomial study variables, confounding factors and the primary study outcomes are presented in Table 2 . Regarding binomial variables, polypharmacy (OR 0.17, 95% CI 0.06–0.45, p < 0.001) and excessive polypharmacy (OR 0.21, 95% CI 0.06–0.70, p = 0.011) were associated with a significantly lower mortality in the adjusted regression model. Furthermore, excessive polypharmacy was associated with a significantly higher risk of ED revisits (OR 2.35, 95% CI 1.12–4.93, p = 0.024). Increasing CCI had a strong independent association with higher mortality (OR 1.70, 95% CI 1.37–2.10, p < 0.001). The results of the logistic regression models including ordinal study variables and CCI are illustrated in Fig. 1 . For mortality, each additional regular medication had an unadjusted OR of 0.96 (95% CI 0.87–1.06, p = 0.419) and an adjusted OR of 0.83 (95% CI 0.72–0.94, p = 0.005) for mortality. For hospital admission, the OR was 1.05 (95% CI 0.99–1.12, p = 0.105) in the unadjusted and 1.04 (95% CI 0.97–1.11, p = 0.263) in the adjusted model. Regarding ED revisits, each additional regular medication had an unadjusted OR of 1.09 (95% CI 1.03–1.16, p = 0.002) and an adjusted OR of 1.09 (95% CI 1.03–1.16, p = 0.014). Each additional PIM had an unadjusted OR of 1.15 (95% CI 0.83–1.60, p = 0.408) and an adjusted OR of 1.00 (95% CI 0.68–1.49, p = 0.990) for mortality. For hospital admission, the unadjusted OR was 1.05 (95% CI 0.85–1.30, p = 0.660) and the adjusted OR was 1.03 (95% CI 0.82–1.28, p = 0.832). Regarding ED revisits, each additional PIM had an unadjusted OR of 1.13 (95% CI 0.93–1.38, p = 0.228) and an adjusted OR of 1.10 (0.89–1.35, p = 0.385). For mortality, an increase of one in CCI had an unadjusted OR of 1.60 (95% CI 1.33–1.91, p < 0.001) and an adjusted OR of 1.70 (95% CI 1.37–2.10, p < 0.001). For hospital admission, the unadjusted OR was 1.15 (95% CI 1.01–1.30, p = 0.035) and the adjusted OR 1.11 (95% CI 0.95–1.30, p = 0.189). The unadjusted OR for ED revisits was 1.15 (95% CI 1.03–1.29, p = 0.016), whereas the adjusted OR was 1.03 (95% CI 0.90–1.18, p = 0.636). Table 2 Unadjusted and adjusted odds ratios of binomial study variables and confounding factors for predicting study outcomes. Variable OR 95% CI p-value 90-day mortality Polypharmacy Adjusted 0.50 0.17 0.23–1.11 0.06–0.45 0.090 < 0.001*** Excessive polypharmacy Adjusted 0.70 0.21 0.28–1.73 0.06–0.70 0.433 0.011* Use of at least one PIM Adjusted 1.40 0.99 0.64–3.06 0.42–2.31 0.399 0.975 CCI Adjusted 1.60 1.70 1.33–1.91 1.37–2.10 < 0.001*** < 0.001*** Age 0.96 0.91–1.01 0.146 Female sex 0.62 0.30–1.29 0.199 eGFR 0.98 0.96-1.00 0.046* Hospital admission following index ED visit OR 95% CI p-value Polypharmacy Adjusted 1.52 1.18 0.91–2.56 0.66–2.13 0.111 0.573 Excessive polypharmacy Adjusted 2.03 1.30 1.09–3.79 0.58–2.88 0.026* 0.526 Use of at least one PIM Adjusted 1.40 0.99 0.64–3.06 0.42–2.31 0.399 0.975 CCI Adjusted 1.15 1.11 1.01–1.30 0.95–1.30 0.035* 0.189 Age 0.98 0.95–1.01 0.265 Female sex 0.80 0.51–1.27 0.344 eGFR 1.00 0.99–1.01 0.991 90-day ED revisit OR 95% CI p-value Polypharmacy Adjusted 1.79 1.58 1.05–3.04 0.88–2.84 0.032* 0.124 Excessive polypharmacy Adjusted 2.17 2.35 1.19–3.97 1.12–4.93 0.012* 0.024* Use of at least one PIM Adjusted 1.15 1.11 0.76–1.75 0.71–1.72 0.502 0.645 CCI Adjusted 1.15 1.03 1.03–1.29 0.90–1.18 0.016* 0.636 Age 1.03 1.00-1.06 0.079 Female sex 0.73 0.48–1.11 0.137 eGFR 0.99 0.99-1.00 0.240 All adjusted regression models include age, sex, eGFR and CCI. Statistically significant p-values are shown in bold, and the level of statistical significance is marked with asterisks (*/**/***). IQR = interquartile range, OR = odds ratio, CI = confidence interval, PIM = potentially inappropriate medication, eGFR = estimated glomerular filtration rate, ED = emergency department * = p < 0.05, ** = p < 0.01, *** = p < 0.001 Recurrent ED visits. Among patients with ED revisits, the patients who used at least one PIM had more ED revisits within follow-up on average than the patients who did not use PIMs [median (IQR) 2 ( 1 – 3 ) vs. 1 ( 1 – 2 ), p = 0.043]. In the Spearman rank correlative analysis, among the patients with ED revisits (n = 165), there was a very weak positive correlation between the number of medications and the number of ED revisits (ρ = 0.16, n = 165, p = 0.042). Additionally, a weak positive correlation was observed between the number of PIMs and the number of ED revisits (ρ = 0.28, n = 165, p < 0.001). Similar positive correlations were observed in the whole study population between the number of regular medications (ρ = 0.19, n = 391, p < 0.001), the number of PIMs (ρ = 0.10, n = 386, p = 0.043) and the number of ED revisits. PIM criteria. In the supplementary analysis, the PIM prevalence according to STOPP/START criteria remained similar to Beers criteria (60% vs 60%) used in the primary analyses. However, PIM prevalence was lower than with Beers, when Meds75 + criteria was used (30% vs 60%). The median number and range of PIMs were similar between the three criteria ( Supplementary Table 1 ). Supplementary Table 2 presents the unadjusted and adjusted odds ratios for logistic regression models including STOPP/START or Meds+ criteria and their association with primary study outcomes. According to STOPP/START criteria, use of an additional PIM predicted a significantly higher risk of 90-day ED revisit in both the unadjusted [OR 1.24, 95% CI (1.04–1.49), p = 0.018) and the adjusted [OR = 1.22, 95% CI (1.01–1.48), p = 0.036) models. Otherwise, PIM use was not associated with significantly lower or higher risks of study outcomes, similarly as with Beers criteria. Discussion Older adults account for at least one-fourth of all ED visits, and their visit rates are rapidly increasing due to global population ageing ( 27 , 28 ). Consequently, geriatric emergency medicine research has become a key focus area in emergency medicine research ( 29 – 31 ). To our knowledge, this is the first study to investigate the effects of both polypharmacy and PIM use on short-term outcomes after ED visit in older patients, simultaneously adjusting for the impact of underlying comorbidities. To assess the effects of number of medications and PIM use beyond the currently used numerical thresholds, we used both binomial and ordinal study variables. Prevalence of polypharmacy and PIM use. Our findings confirm that polypharmacy is highly prevalent among older ED patients: 80% of the included patients used five or more medications regularly, while 30% of the patients used 10 or more. This aligns with global and national estimates among community-dwelling older adults, additionally reflecting a rapid growth in polypharmacy prevalence ( 11 , 32 ). The prevalence of PIM use in our study was consistent with both global and national estimates among community-dwelling older adults, suggesting a prevalence of approximately 37% ( 33 , 34 ). Some studies have reported a lower prevalence of polypharmacy in ED setting: Van Dam et al reported prevalence of 43% for polypharmacy and 18% for excessive polypharmacy, whereas Ruiz Ramos et al reported prevalences of 41% and 22%, respectively ( 22 , 35 ). These observed differences could be linked to variance in the study populations, local prescribing practice or data collection schemes. Polypharmacy and study outcomes. Unexpectedly, our study found that polypharmacy did not predict a higher risk of mortality or hospital admissions from the ED when confounding factors including morbidity were considered. In fact, polypharmacy was associated with a lower risk of mortality, and the result was confirmed in two adjusted regression models using binomial and ordinal variables as determinants of polypharmacy. It is possible that the polypharmacy patients in this study represented a selected healthier group of patients for whom the use of multiple medications is considered safe. However, the lower mortality risk associated with polypharmacy may also reflect the prognosis-improving effects of “appropriate polypharmacy. In recent years, multiple studies have suggested that polypharmacy should not inherently be regarded as harmful and medication reviews should focus on medication appropriateness instead of the absolute number of medications ( 11 , 32 , 36 , 37 ). On the other hand, we found that excessive polypharmacy and increasing number of medications predicted a higher risk of ED revisits. The potential negative effects of excessive medication in outpatient setting have been recognized in previous studies, suggesting that excessive polypharmacy is associated with drug interactions and unplanned hospitalizations among older people ( 38 , 39 ). In ED setting, a study by Salvi et al. found that the use of ≥ 10 medications was associated with higher risk of early and late ED revisits, late hospital admissions and death in older patients ( 40 ). Aligning with these findings, our study results indicate that excessive polypharmacy is a stronger risk factor than polypharmacy for predicting negative outcomes such as ED revisits among older ED patients. In contrast to our findings, Van Dam et al. found that polypharmacy was an independent predictor for 90-day mortality but not for ED revisits ( 22 ). These differences may be caused by the earlier mentioned distinctions in polypharmacy prevalence, which may result from the significantly different study populations: van Dam et al. included patients aged ≥ 70 years who gave written consent, whereas our study included patients aged ≥ 75 years and patient consent was not required. Lastly, van Dam et al. adjusted for ISAR-HP – a frailty screening tool originally developed to detect functional decline in hospitalized older patients – to correct for frailty in the analyses ( 41 ). Regarding the previously reported poor ability of ISAR-HP to detect frailty, and the significant decline resulting from adjusting for ISAR-HP in the predictive value of polypharmacy for ED revisits in their study, including ISAR-HP in the analyses may have confounded the results on ED revisits ( 22 , 41 ). On the other hand, we acknowledge that we were unable to consider frailty due to the retrospective nature of our study. PIM use and study outcomes. Our findings regarding the associations between PIM use and short-term adverse outcomes are consistent with previous studies assessing long-term outcomes, as they have found no association between PIM use, mortality, or ED revisits ( 12 ). Considering that previous literature has reported a large variance in PIM prevalence among different PIM criteria, we performed supplementary analyses using the European STOPP/START and the Finnish Meds75 + criteria in addition to Beers criteria ( 34 ). In these analyses, in contrast to Beers or Meds75+, each additional STOPP/START PIM was associated with a significantly higher risk of ED revisits. Otherwise, there were no significant associations between PIM use and study outcomes, regardless of the criteria. Despite being beneficial in outpatient setting, assessing appropriateness of individual medications of an older patient with polypharmacy remains challenging in ED setting, and missing or inaccurate medication lists further complicate the matter ( 42 ). Although pharmacist-led medication review/reconciliation has shown some benefits also in the context of acute care ( 43 , 44 ), the approach to reduce PIM use in ED setting has not resulted in better clinical outcomes ( 45 ). In the light of previous literature and our study results, we suggest that PIMs most likely play a role in the ED revisits of older ED patients, but identifying individual PIMs and interactions with patient characteristics may be of greater importance as identifying PIMs as a group in ED setting. Comprehensive medication reviews are beneficial but should be performed non-urgently for high-risk patients, for example in home care, where more time and information are available. This kind of thorough outpatient medication review would additionally prevent unnecessary ED visits for frail older adults ( 46 ). However, the standardized methods to identify these high-risk patients in ED settings remain unclear. Frequent ED visits. According to Spearman correlative analysis, polypharmacy and PIM use had a positive correlation with the number of ED revisits, suggesting that older patients with drug-related problems may be at a higher risk of frequent ED use. However, the statistical power of the analysis remained low, and therefore these results should be confirmed in larger samples. It is, however, understandable that DRPs lead to repeat ED visits especially if the underlying medication-related issues are not identified and managed at the ED. Even though medication-related problems may not be resolved at the ED, identifying them or the patients requiring medication review would help referring patients to appropriate services to reduce future health care needs. Current frailty screening and risk stratification tools. As mentioned earlier, international geriatric emergency medicine guidelines recommend screening for polypharmacy and PIM use in the EDs ( 18 , 19 ). Furthermore, polypharmacy screening is included in several risk and frailty screening tools designed for ED setting ( 20 , 21 ). However, since the numerical threshold for defining polypharmacy is ≥ 3 medications in the ISAR tool and ≥ 5 in the TRST, including polypharmacy with these thresholds as an item may even reduce the positive predictive value of these tools, which have recently shown poor prognostic accuracy in identifying frailty or high-risk older patients ( 47 , 48 ). Considering these findings and the high prevalence of the use of five or more regular medications among older ED patients, we suggest the numerical threshold for polypharmacy screening should be updated to higher such as 10 medications or over, or more emphasis should be paid on certain medications or drug classes associated with the highest risks ( 11 , 49 ). Implementations for the future. Our results indicate that underlying morbidity is a strong determinant of adverse outcomes among older ED patients. However, the association between the use of five or more medications and these outcomes is weak, aligning with the increasing evidence that polypharmacy is often inevitable to reach therapeutic equilibrium in chronic illness. However, as we found that use of ten or more medications is associated with ED revisits, we propose that polypharmacy screening should be performed in ED setting but with novel approaches. However, more prospective studies are needed to not only determine the optimal polypharmacy thresholds but also to confirm the benefits of medication screening in the ED. Strengths and limitations We incorporated a large, high-quality dataset of older ED patients. To limit the impact and evaluate the accuracy of current numerical thresholds, we evaluated medication use with both binomial and ordinal variables. Additionally, we defined CCI for each patient to consider the effect of underlying comorbidities to study outcomes. Furthermore, to evaluate the differences between the most common PIM criteria, separate supplementary analyses for each criterion were performed. However, limitations of this study must be acknowledged. Although we adjusted for multiple patient-specific confounding factors in the analysis, the impact of underlying comorbidities could not be entirely removed. As no frailty measurement was carried out at the ED, we could not adjust for frailty. Furthermore, due to retrospective study design and limited information of the indications for medication use, we could not recognize DRPs and ADEs, medication timeliness or patient adherence. Therefore, pro re nata (PRN) medications were not included in the data analysis. Medication use after the ED visit could not be measured, which should also be considered as medication administration practices could have changed during the follow-up. Lastly, the study population did not include older adults living in nursing homes or long-term care facilities – thus, the study results should be considered only in the population of community-dwelling older adults. Conclusions Polypharmacy and PIM use are common among older ED patients. With the threshold of ≥ 5, polypharmacy does not predict higher risk of study outcomes but predicts a lower mortality risk. Excessive polypharmacy and each addition in the number of medications predicts a higher risk of ED revisits. PIM use does not predict adverse outcomes. Current approaches for polypharmacy and PIM screening in ED setting should be updated to identify high-risk older patients. Abbreviations ACEP American College of Emergency Physicians ADE adverse drug event CCI Charlson Comorbidity Index CI confidence interval DRP drug-related problem ED emergency department eGFR estimated glomerular filtration rate EM emergency medicine EMS emergency medical services EuGMS European Geriatric Medicine Society EUSEM European Society of Emergency Medicine HUH Helsinki University Hospital ICD-10 10th revision of International Statistical Classification of Diseases and Related Health Problems IQR interquartile range ISAR Identification of Seniors At Risk OR odds ratio PIM potentially inappropriate medication PRN pro re nata SD standard deviation STOPP/START Screening Tool of Older Persons' Prescriptions / Screening Tool to Alert to Right Treatment TRST Triage Risk Screening Tool Declarations Ethics Approval and Consent to Participate The study design was approved by the Helsinki University Hospital (HUH) District (Ref. no. § 85 HUS/223/2023). All procedures performed in this study were in accordance with the Declaration of Helsinki. Due to the retrospective and observational nature of the study, patient consent or approval from the HUH Regional Committee on Medical Research Ethics was not required. Consent for Publication Not applicable. Funding This study was funded by the public government research fund of Helsinki University Hospital Emergency Medicine and Services. Author Contribution RH gathered the medication lists of included patients and combined it with the previously collected clinical data, took part in the conceptualization, performed the statistical analysis, created the figures and tables and prepared the initial and final version of the manuscript. MH took part in planning and conceptualization of the study and assisted with statistical analysis and demonstration of the results. ES gathered the initial clinical data of included patients and assisted in preparing the manuscript. EJ, MC and JK took part in planning, conceptualization and project coordination and reviewing the manuscript. KK took part in planning the project and consulted the pharmacological aspects of the study. All authors have reviewed and approved the final version of the manuscript. Acknowledgement We acknowledge Helsinki University Hospital Emergency Medicine and Services, Faculty of Medicine in University of Helsinki and Emergency Medical Services (EMS) of Helsinki University Hospital area for the effortless collaboration regarding this research project. Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. References Nymoen LD, Björk M, Flatebø TE, Nilsen M, Godø A, Øie E, et al. Drug-related emergency department visits: prevalence and risk factors. Intern Emerg Med. 2022;17(5):1453–62. 10.1007/s11739-022-02935-9 . PubMed PMID: 35129789. 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Ann Emerg Med. 2001;38(6):666–71. 10.1067/mem.2001.119456 . PubMed PMID: 11719747. Nickel CH, Ruedinger JM, Messmer AS, Maile S, Peng A, Bodmer M, et al. Drug - related emergency department visits by elderly patients presenting with non-specific complaints. Scand J Trauma Resusc Emerg Med. 2013;21(1). 10.1186/1757-7241-21-15 . PubMed PMID: 23497667. Khezrian M, McNeil CJ, Murray AD, Myint PK. An overview of prevalence, determinants and health outcomes of polypharmacy. Ther Adv Drug Saf. 2020;11:1–10. 10.1177/2042098620933741 . Mekonnen AB, Redley B, de Courten B, Manias E. Potentially inappropriate prescribing and its associations with health-related and system-related outcomes in hospitalised older adults: A systematic review and meta-analysis. Br J Clin Pharmacol. 2021;87(11):4150–72. 10.1111/bcp.14870 . PubMed PMID: 34008195. Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17(1). 10.1186/s12877-017-0621-2 . PubMed PMID: 29017448. O’mahony D, O’sullivan D, Byrne S, O’connor MN, Ryan C, Gallagher P. STOPP/START criteria for potentially inappropriate prescribing in older people: Version 2. Age Ageing. 2015;44(2):213–8. 10.1093/ageing/afu145 . PubMed PMID: 25324330. American Geriatrics Society. 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc. 2019;67(4):674–94. 10.1111/jgs.15767 . Ellis G, Gardner M, Tsiachristas A, Langhorne P, Burke O, Harwood RH, et al. Comprehensive geriatric assessment for older adults admitted to hospital. Cochrane Database Syst Reviews. 2017;2017(9). 10.1002/14651858.CD006211.pub3 . Veronese N, Custodero C, Demurtas J, Smith L, Barbagallo M, Maggi S, et al. Comprehensive geriatric assessment in older people: an umbrella review of health outcomes. Age Ageing. 2022;51(5). 10.1093/ageing/afac104 . Lucke JA, Mooijaart SP, Heeren P, Singler K, McNamara R, Gilbert T, et al. Providing care for older adults in the Emergency Department: expert clinical recommendations from the European Task Force on Geriatric Emergency Medicine. Eur Geriatr Med. 2022;13(2):309–17. 10.1007/s41999-021-00578-1 . PubMed PMID: 34738224. Rosenberg M, Carpenter C, Bromley M. Geriatric Emergency Department Guidelines. 2013. McCusker J, Bellavance F, Cardin S, Trépanier S, Verdon J, Ardman O. Detection of older people at increased risk of adverse health outcomes after an emergency visit: The ISAR screening tool. J Am Geriatr Soc. 1999;47(10):1229–37. 10.1111/j.1532-5415.1999.tb05204 . x PubMed PMID: 10522957. Meldon SW, Mion LC, Palmer RM, Drew BL, Connor JT, Lewicki LJ, et al. A Brief Risk-stratification Tool to Predict Repeat Emergency Department Visits and Hospitalizations in Older Patients Discharged from the Emergency Department. Acad Emerg Med. 2003;10(3):224–32. 10.1111/j.1553-2712.2003.tb01996.x . van Dam CS, Labuschagne HA, van Keulen K, Kramers C, Kleipool EE, Hoogendijk EO, et al. Polypharmacy, comorbidity and frailty: a complex interplay in older patients at the emergency department. Eur Geriatr Med. 2022;13(4):849–57. 10.1007/s41999-022-00664-y . PubMed PMID: 35723840. Saario EL, Mäkinen MT, Jämsen ERK, Nikander P, Castrén MK. Screening of community-dwelling older patients by the emergency medical services: An observational retrospective registry study. Int Emerg Nurs. 2021;59. 10.1016/j.ienj.2021.101078 . PubMed PMID: 34571450. Fick DM, Cooper JW, Wade WE, Waller JL, Maclean J, Ross, Beers MH. Updating the Beers Criteria for Potentially Inappropriate Medication Use in Older Adults Results of a US Consensus Panel of Experts. Arch Intern Med. 2003;163(22):2716–24. Charlson ME, Pompei P, Ales KL, Mackenzie CR. A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation. J Chronic Dis. 1987;40(5):373–83. Jyrkkä J, Paulamäki J, Hartikainen S, Ahonen J, Antikainen R, Jauhonen HM, et al. Prescribing Appropriate Medicines to Older Adults: A Finnish Experience with the Web-Based Meds75 + Database. Drugs Aging. 2024;41(8):665–74. 10.1007/s40266-024-01131-y . Samaras N, Chevalley T, Samaras D, Gold G. Older patients in the emergency department: A review. Ann Emerg Med. 2010;56(3):261–9. 10.1016/j.annemergmed.2010 . 04.015 PubMed PMID: 20619500. United Nations Department of Economic and Social Affairs PD. World Population Prospects 2022: Summary of Results. 2022. Mooijaart SP, Lucke JA, Brabrand M, Conroy S, Nickel CH. Geriatric emergency medicine: Time for a new approach on a European level. Eur J Emerg Med. 2019;26(2):75–6. 10. 1097/MEJ.0000000000000594 PubMed PMID: 30801429. Smith J, Keating L, Flowerdew L, O’Brien R, McIntyre S, Morley R, et al. An Emergency Medicine Research Priority Setting Partnership to establish the top 10 research priorities in emergency medicine. Emerg Med J. 2017;34(7):454–6. 10.1136/emermed-2017-206702 . PubMed PMID: 28473529. Crilly J, Huang Y, Krahe M, Wilhelms D, Ekelund U, Hörlin E, et al. Research priority setting in emergency care: A scoping review. JACEP Open. 2022;3(6):e12852. 10.1002/emp2.12852 . Delara M, Murray L, Jafari B, Bahji A, Goodarzi Z, Kirkham J, et al. Prevalence and factors associated with polypharmacy: a systematic review and Meta-analysis. BMC Geriatr. 2022;22(1). 10.1186/s12877-022-03279-x . PubMed PMID: 35854209. Tian F, Chen Z, Zeng Y, Feng Q, Chen X. Prevalence of Use of Potentially Inappropriate Medications among Older Adults Worldwide: A Systematic Review and Meta-Analysis. JAMA Netw Open. 2023;6(8):E2326910. 10.1001/jamanetworkopen.2023.26910 . PubMed PMID: 37531105. Paulamäki J, Jyrkkä J, Hyttinen V, Jämsen E. Prevalence of potentially inappropriate medication use in older population: comparison of the Finnish Meds75 + database with eight published criteria. BMC Geriatr. 2023;23(1). 10.1186/s12877-022-03706-z . PubMed PMID: 36899320. Ruiz Ramos J, Alquézar-Arbé A, Juanes Borrego A, Burillo Putze G, Aguiló S, Jacob J, et al. Short-term prognosis of polypharmacy in elderly patients treated in emergency departments: results from the EDEN project. Ther Adv Drug Saf. 2024;15. 10.1177/20420986241228129 . Koehl JL. Adverse Drug Event Prevention and Detection in Older Emergency Department Patients. Clin Geriatr Med. 2023;39(4):635–45. 10.1016/j.cger.2023.04.008 . Rankin A, Cadogan CA, Patterson SM, Kerse N, Cardwell CR, Bradley MC, et al. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database Syst Reviews. 2018;2018(9). 10.1002/14651858.CD008165.pub4 . PubMed PMID: 30175841. Sánchez-Fidalgo S, Guzmán-Ramos MI, Galván-Banqueri M, Bernabeu-Wittel M, Santos-Ramos B. Prevalence of drug interactions in elderly patients with multimorbidity in primary care. Int J Clin Pharm. 2017;39(2):343–53. 10.1007/s11096-017-0439-1 . Rönneikkö JK, Jämsen ER, Mäkelä M, Finne-Soveri H, Valvanne JN. Reasons for home care clients’ unplanned Hospital admissions and their associations with patient characteristics. Arch Gerontol Geriatr. 2018;78:114–26. 10.1016/j.archger.2018.06.008 . Salvi F, Rossi L, Lattanzio F, Cherubini A. Is polypharmacy an independent risk factor for adverse outcomes after an emergency department visit? Intern Emerg Med. 2017;12(2):213–20. 10.1007/s11739-016-1451-5 . PubMed PMID: 27075646. van Dam CS, Trappenburg MC, ter Wee MM, Hoogendijk EO, de Vet R, Smulders YM, et al. The Prognostic Accuracy of Clinical Judgment Versus a Validated Frailty Screening Instrument in Older Patients at the Emergency Department: Findings of the AmsterGEM Study. Ann Emerg Med. 2022;80(5):422–31. 10.1016/j.annemergmed.2022.04.039 . Schepel L, Lehtonen L, Airaksinen M, Ojala R, Ahonen J, Lapatto-Reiniluoto O. Medication reconciliation and review for older emergency patients requires improvement in Finland. Int J Risk Saf Med. 2019;30(1):19–31. 10.3233/JRS-180030 . Atey TM, Peterson GM, Salahudeen MS, Bereznicki LR, Wimmer BC. Impact of pharmacist interventions provided in the emergency department on quality use of medicines: a systematic review and meta-analysis. Emerg Med J. 2023;40(2):120–7. 10.1136/emermed-2021-211660 . Hellinger BJ, Gries A, Schiek S, Remane Y, Bertsche T. A prospective intervention study to identify drug-related emergency department visits comparing a standard care group and a pharmaceutical care group. Eur J Emerg Med. 2024;31(1):9–17. 10. 1097/MEJ.0000000000001070 PubMed PMID: 37650724. Santolaya-Perrín R, Calderón-Hernanz B, Jiménez-Díaz G, Galán-Ramos N, Moreno-Carvajal MT, Rodríguez-Camacho JM, et al. The efficacy of a medication review programme conducted in an emergency department. Int J Clin Pharm. 2019;41(3):757–66. 10.1007/s11096-019-00836-0 . PubMed PMID: 31028596. McCusker J, Verdon J, Do Geriatric Interventions Reduce Emergency Department Visits? A Systematic Review. Journals Gerontology: Ser A. 2006;61(1):53–62. 10.1093/gerona/61.1.53 . van Dam CS, Trappenburg MC, ter Wee MM, Hoogendijk EO, de Vet HC, Smulders YM, et al. The Accuracy of Four Frequently Used Frailty Instruments for the Prediction of Adverse Health Outcomes Among Older Adults at Two Dutch Emergency Departments: Findings of the AmsterGEM Study. Ann Emerg Med. 2021;78(4):538–48. 10.1016/j.annemergmed.2021.04.027 . Moloney E, O’Donovan MR, Sezgin D, Flanagan E, McGrath K, Timmons S, et al. Diagnostic Accuracy of Frailty Screening Instruments Validated for Use among Older Adults Attending Emergency Departments: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2023;20(13):6280. 10.3390/ijerph20136280 . Rasmussen LF, Grode L, Barat I, Gregersen M. Prevalence of factors contributing to unplanned hospital readmission of older medical patients when assessed by patients, their significant others and healthcare professionals: a cross-sectional survey. Eur Geriatr Med. 2023;14(4):823–35. 10.1007/s41999-023-00799-6 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9326583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630258348,"identity":"d9d0bed8-c208-400a-9f3d-edcb2f33ac9a","order_by":0,"name":"Ria Holstein","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PsYrCMBjA8YSALolZU+pDJB4oN92DODoHnKSDhoKgy4FrBs97hXPpXAnUpeAquHRyEzqJo6ngclBbN8H8h/DxkR8hALhcrxgCDQCGtwFmeVAMKKxBOGB2QEKnxQZWEHAndvDJrFhUkN4cHbOcK0WbJPHJatKnc0suQVRK2qbRE5ob5k1bg8+faCu1gSH8Tg+lhCHQ9TGPGTdY7E9RIkNLEJw9Is2zJYp9GcwZWSbyt5rg4hXEOMIdj4Rj+VeDjDz7F08b/CF0Esu1JZuHf6HbiOWBonSRiiwfK7namU12CcrJ/8ztjGvft6lnLrtcLtebdAXox1DaVCfhlwAAAABJRU5ErkJggg==","orcid":"","institution":"Helsinki University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ria","middleName":"","lastName":"Holstein","suffix":""},{"id":630258350,"identity":"b71a5de4-6a32-4ef2-b106-2a20ddaceacf","order_by":1,"name":"Mari Hongisto","email":"","orcid":"","institution":"Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mari","middleName":"","lastName":"Hongisto","suffix":""},{"id":630258351,"identity":"0744bdd3-2140-4dfd-a3d8-ed9cc8b17e58","order_by":2,"name":"Esa Jämsen","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Esa","middleName":"","lastName":"Jämsen","suffix":""},{"id":630258353,"identity":"00bcaffb-81d3-4eea-b77c-23df72c7c2c9","order_by":3,"name":"Eeva Saario","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Eeva","middleName":"","lastName":"Saario","suffix":""},{"id":630258355,"identity":"b5dd4be6-c197-42e8-90d0-d3f87b564bf0","order_by":4,"name":"Kirsi Kvarnström","email":"","orcid":"","institution":"Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kirsi","middleName":"","lastName":"Kvarnström","suffix":""},{"id":630258356,"identity":"68be1eaa-8550-48e1-ab8b-b020ce5e2fc5","order_by":5,"name":"Maaret Castrén","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Maaret","middleName":"","lastName":"Castrén","suffix":""},{"id":630258357,"identity":"f168c28a-0294-407c-be75-71339f77d018","order_by":6,"name":"Johanna Kaartinen","email":"","orcid":"","institution":"Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Kaartinen","suffix":""}],"badges":[],"createdAt":"2026-04-05 13:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9326583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9326583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108030001,"identity":"5dda1f7f-f05a-4c75-a81c-3efc8a039702","added_by":"auto","created_at":"2026-04-28 15:51:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54518,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of unadjusted and adjusted associations between the ordinal study variables, CCI and A) mortality, B) hospital admission following index ED presentation and C) ED revisit.\u003c/p\u003e\n\u003cp\u003eAll adjusted regression models include age, sex, eGFR and CCI. The statistically significant results are marked with asterisks, which also show the level of statistical significance (*/**/***).\u003c/p\u003e\n\u003cp\u003ePIM = potentially inappropriate medication, CCI = Charlson Comorbidity Index, ED = emergency department, OR = odds ratio, CI = confidence interval\u003cbr\u003e\n * = p\u0026lt;0.05, ** = p\u0026lt;0.01, *** = p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9326583/v1/d4695c3e26e1e948cb61b402.png"},{"id":108183802,"identity":"d0fa293f-12df-4141-82cf-de5bc14c77a4","added_by":"auto","created_at":"2026-04-30 09:02:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":489289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9326583/v1/ee2c77f6-665e-4846-bfed-fac2026303b1.pdf"},{"id":108182012,"identity":"8f039b3f-d71f-4152-928d-c4443cba7ccb","added_by":"auto","created_at":"2026-04-30 08:59:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18146,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9326583/v1/280d160b8ad61832f2f63bd2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Polypharmacy and Inappropriate Medication Use in Older Emergency Department Patients: A Retrospective Observational Study","fulltext":[{"header":"Background","content":"\u003cp\u003eDrug-related problems (DRP), such as adverse drug events (ADE), are estimated to account for over one-fifth of all emergency department (ED) visits (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite their high prevalence, a significant proportion of drug-related ED visits remain unrecognized, increasing the risks of misdiagnosis and ED readmission (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOlder ED patients are at a high risk of drug-related ED visits (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). This vulnerability is largely attributed to polypharmacy and potentially inappropriate medication (PIM) use, both of which are common in this population and are well-established risk factors for DRPs, increased healthcare use and hospitalizations (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). Although there is variance among definitions, previous literature has classically defined polypharmacy as the use of five or more regular medications, and excessive polypharmacy as the use of ten or more regular medications (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). PIMs, again, are medications in which the potential risks outweigh the expected benefits among older individuals. PIMs are often defined with explicit criteria, of which the most common are the American Beers or the European Screening Tool of Older Persons' Prescriptions / Screening Tool to Alert to Right Treatment (STOPP/START) criteria (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith growing recognition of the unique needs of older adults, polypharmacy and PIM screening have become crucial components in the medication reviews of comprehensive geriatric assessment (CGA) in both outpatient and inpatient settings (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). Consequently, geriatric acute care guidelines developed by the American College of Emergency Physicians (ACEP), the European Society of Emergency Medicine (EUSEM) and the European Geriatric Medicine Society (EuGMS) have recommended that polypharmacy and PIM screening as a part of geriatric assessments in ED setting (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, polypharmacy screening is included as an item in several ED-based frailty screening tools, such as Identification of Seniors at Risk (ISAR) and the Triage Risk Screening Tool (TRST) (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, evidence of the predictive value of polypharmacy and PIM use on short-term adverse outcomes as well as of the benefits of their screening in ED setting remains limited. To our knowledge, only one study has evaluated polypharmacy as a predictor of short-term adverse outcomes among older ED patients while adjusting for underlying comorbidities, and no comparable studies have been conducted regarding PIM use (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, the aim of this study was to determine the predictive value of polypharmacy and PIM use for adverse outcomes among older ED patients. We hypothesized that both polypharmacy and PIM use would be associated with increased risks of mortality, hospital admissions following the index ED visit, and ED revisits.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy design, setting and population.\u003c/b\u003e This retrospective, single center observational study was conducted in Espoo, Finland (population of approx. 300,000). The study population was derived from a previously established cohort of an emergency medical services (EMS) risk screening study, which included community-dwelling adults aged ≥ 70 years transported to the ED by EMS between 10th November 2018 and 30th July 2019 (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). For this study, a subpopulation of adults aged ≥ 75 years was included. Nine patients were excluded due to multiple ED visits during the study period or missing health records from the index ED visit. All data was collected retrospectively from electronic health records.\u003c/p\u003e\u003cp\u003e \u003cb\u003eVariables\u003c/b\u003e. For each included patient, the medications used at the time of the index ED visit were recorded from the most recent medication lists. Medications were defined as medicinal substances regularly administered to treat or prevent disease via oral, transdermal, subcutaneous, or inhalation routes. Regularly used supplements and vitamins were also included. Medications administered or prescribed during the index ED visit were excluded. Medication inclusion criteria were established in consultation with a clinical pharmacist.\u003c/p\u003e\u003cp\u003ePolypharmacy and PIM use were measured with both binomial and ordinal variables. Polypharmacy was defined as the regular use of ≥ 5 medications, and excessive polypharmacy as ≥ 10 (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). Additionally, the total number of regularly used medications was determined. Respectively, PIM use was defined as the use of at least one PIM and as the total number of PIMs. PIMs were defined according to the 2019 Beers criteria (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo measure the burden of comorbidities, age-adjusted Charlson Comorbidity Index (CCI) was calculated for each patient. CCI ranges from a minimum of 3 to a maximum of 37 (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). Furthermore, estimated glomerular filtration rates (eGFR), based on the most recently measured serum creatinine and CKD-EPI equation, were calculated. The primary diagnoses and complaints of the ED visits were classified according to the 10th revision of International Statistical Classification of Diseases and Related Health Problems (ICD-10). All data was derived from the electronic health care records of the included patients.\u003c/p\u003e\u003cp\u003e \u003cb\u003eOutcomes.\u003c/b\u003e The primary study outcomes were mortality, hospital admissions following index ED presentation and ED revisits during the follow-up. The follow-up time was 90 days. Revisits were defined as any ED visit occurring within the follow-up at any of the EDs of the Helsinki University Hospital (HUH). The revisits were identified from electronic hospital databases and confirmed from medical charts. The total number of ED revisits during the follow-up was used as a secondary outcome.\u003c/p\u003e\u003cp\u003e \u003cb\u003eStatistical Analysis.\u003c/b\u003e Categorical variables are reported as counts (N) and percentages (%), parametric continuous variables as means and standard deviations (SD) and non-parametric continuous variables as medians and interquartile ranges (IQR). Shapiro-Wilk test was used to test for normality of continuous variables. For the comparison between continuous variables, Mann-Whitney U test was used. Logistic regression models were used to assess associations between study variables and primary outcomes. Adjusted models included age, sex, CCI and renal function (eGFR) as confounding factors. The results are presented as odds ratios (OR) with 95% confidence intervals (CI). Associations between study variables and the number of ED revisits were analyzed using Spearman rank correlation. A p-value \u0026lt; 0.05 was considered statistically significant. The statistical analyses were performed with RStudio and IBM SPSS Statistics (versions 28 and 29). Graphical illustrations were generated with GraphPad Prism (v9.0.0.121).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSupplementary analysis.\u003c/b\u003e Finally, separate analyses and logistic regression models were analyzed using the STOPP/START (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) and the Finnish Meds75+ (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e) as PIM criteria instead of the Beers criteria.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 392 older adults with a median age of 84 years (IQR 79\u0026ndash;89) were included. Their baseline data are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Two-thirds of the patients were female. The median CCI was 6 (IQR 5\u0026ndash;7), with a maximum of 12. Nonspecific clinical signs or complaints (ICD-10 R00-R99) accounted for 36% (n\u0026thinsp;=\u0026thinsp;141) of all ED diagnoses, and traumatic diagnoses (ICD-10 S00-T98) for 27% (n\u0026thinsp;=\u0026thinsp;106). The most common single diagnosis was malaise and fatigue (ICD-10 R53), accounting for 13% of all diagnoses. One-fifth of the visits (n\u0026thinsp;=\u0026thinsp;77) were related to falls.\u003c/p\u003e\n\u003cp\u003ePolypharmacy was present in 80% of patients, and excessive polypharmacy in 30%. The median number of regular medications was 7 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e), ranging from 0 to 20. A total of 234 (60%) patients used at least one PIM, with a maximum of 7 PIMs.\u003c/p\u003e\n\u003cp\u003eDuring the 90-day follow-up, 32 patients (8.2%) died, 269 (69%) were admitted to hospital after the index ED visit, and 165 (42%) revisited the ED at least once after the index ED visit. Of the patients with ED revisits, 71 (43%) visited the ED twice or more.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of the included patients (N\u0026thinsp;=\u0026thinsp;392).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN (%) or median (IQR)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84 (79\u0026ndash;89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e263 (67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCI, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDementia, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83 (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeGFR, ml/min, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (43\u0026ndash;79)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of regular medications, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePolypharmacy, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e315 (80)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcessive polypharmacy, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of at least one PIM, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234 (60)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of PIMs, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the ED visits\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMost common ED diagnoses (ICD-10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026bull; R53 Malaise and fatigue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026bull; S01 Open wound of head\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026bull; I48 Atrial fibrillation and flutter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (4.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026bull; R42 Dizziness and giddiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (4.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026bull; J18 Pneumonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED visit involving a fall, N (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77 (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eIQR\u0026thinsp;=\u0026thinsp;interquartile range, CCI\u0026thinsp;=\u0026thinsp;Charlson Comorbidity Index, eGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate, PIM\u0026thinsp;=\u0026thinsp;potentially inappropriate medication, ED\u0026thinsp;=\u0026thinsp;emergency department, ICD-10\u0026thinsp;=\u0026thinsp;International Classification of Diseases, Tenth Revision\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic regression.\u003c/strong\u003e The unadjusted and adjusted associations between binomial study variables, confounding factors and the primary study outcomes are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Regarding binomial variables, polypharmacy (OR 0.17, 95% CI 0.06\u0026ndash;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and excessive polypharmacy (OR 0.21, 95% CI 0.06\u0026ndash;0.70, p\u0026thinsp;=\u0026thinsp;0.011) were associated with a significantly lower mortality in the adjusted regression model. Furthermore, excessive polypharmacy was associated with a significantly higher risk of ED revisits (OR 2.35, 95% CI 1.12\u0026ndash;4.93, p\u0026thinsp;=\u0026thinsp;0.024). Increasing CCI had a strong independent association with higher mortality (OR 1.70, 95% CI 1.37\u0026ndash;2.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eThe results of the logistic regression models including ordinal study variables and CCI are illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. For mortality, each additional regular medication had an unadjusted OR of 0.96 (95% CI 0.87\u0026ndash;1.06, p\u0026thinsp;=\u0026thinsp;0.419) and an adjusted OR of 0.83 (95% CI 0.72\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.005) for mortality. For hospital admission, the OR was 1.05 (95% CI 0.99\u0026ndash;1.12, p\u0026thinsp;=\u0026thinsp;0.105) in the unadjusted and 1.04 (95% CI 0.97\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.263) in the adjusted model. Regarding ED revisits, each additional regular medication had an unadjusted OR of 1.09 (95% CI 1.03\u0026ndash;1.16, p\u0026thinsp;=\u0026thinsp;0.002) and an adjusted OR of 1.09 (95% CI 1.03\u0026ndash;1.16, p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\n\u003cp\u003eEach additional PIM had an unadjusted OR of 1.15 (95% CI 0.83\u0026ndash;1.60, p\u0026thinsp;=\u0026thinsp;0.408) and an adjusted OR of 1.00 (95% CI 0.68\u0026ndash;1.49, p\u0026thinsp;=\u0026thinsp;0.990) for mortality. For hospital admission, the unadjusted OR was 1.05 (95% CI 0.85\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.660) and the adjusted OR was 1.03 (95% CI 0.82\u0026ndash;1.28, p\u0026thinsp;=\u0026thinsp;0.832). Regarding ED revisits, each additional PIM had an unadjusted OR of 1.13 (95% CI 0.93\u0026ndash;1.38, p\u0026thinsp;=\u0026thinsp;0.228) and an adjusted OR of 1.10 (0.89\u0026ndash;1.35, p\u0026thinsp;=\u0026thinsp;0.385).\u003c/p\u003e\n\u003cp\u003eFor mortality, an increase of one in CCI had an unadjusted OR of 1.60 (95% CI 1.33\u0026ndash;1.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an adjusted OR of 1.70 (95% CI 1.37\u0026ndash;2.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For hospital admission, the unadjusted OR was 1.15 (95% CI 1.01\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.035) and the adjusted OR 1.11 (95% CI 0.95\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.189). The unadjusted OR for ED revisits was 1.15 (95% CI 1.03\u0026ndash;1.29, p\u0026thinsp;=\u0026thinsp;0.016), whereas the adjusted OR was 1.03 (95% CI 0.90\u0026ndash;1.18, p\u0026thinsp;=\u0026thinsp;0.636).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eUnadjusted and adjusted odds ratios of binomial study variables and confounding factors for predicting study outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e90-day mortality\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePolypharmacy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23\u0026ndash;1.11\u003c/p\u003e\n\u003cp\u003e0.06\u0026ndash;0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001***\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcessive polypharmacy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u0026ndash;1.73\u003c/p\u003e\n\u003cp\u003e0.06\u0026ndash;0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.433\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.011*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of at least one PIM\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64\u0026ndash;3.06\u003c/p\u003e\n\u003cp\u003e0.42\u0026ndash;2.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.399\u003c/p\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCI\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.60\u003c/p\u003e\n\u003cp\u003e1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33\u0026ndash;1.91\u003c/p\u003e\n\u003cp\u003e1.37\u0026ndash;2.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001***\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u0026ndash;1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.146\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.30\u0026ndash;1.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeGFR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96-1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.046*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHospital admission following index ED visit\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePolypharmacy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.52\u003c/p\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u0026ndash;2.56\u003c/p\u003e\n\u003cp\u003e0.66\u0026ndash;2.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003cp\u003e0.573\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcessive polypharmacy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.03\u003c/p\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09\u0026ndash;3.79\u003c/p\u003e\n\u003cp\u003e0.58\u0026ndash;2.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.026*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of at least one PIM\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64\u0026ndash;3.06\u003c/p\u003e\n\u003cp\u003e0.42\u0026ndash;2.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.399\u003c/p\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCI\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u0026ndash;1.30\u003c/p\u003e\n\u003cp\u003e0.95\u0026ndash;1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.035*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e0.189\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95\u0026ndash;1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.265\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51\u0026ndash;1.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.344\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeGFR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e90-day ED revisit\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePolypharmacy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.79\u003c/p\u003e\n\u003cp\u003e1.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05\u0026ndash;3.04\u003c/p\u003e\n\u003cp\u003e0.88\u0026ndash;2.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.032*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcessive polypharmacy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.17\u003c/p\u003e\n\u003cp\u003e2.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19\u0026ndash;3.97\u003c/p\u003e\n\u003cp\u003e1.12\u0026ndash;4.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.012*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.024*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of at least one PIM\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u0026ndash;1.75\u003c/p\u003e\n\u003cp\u003e0.71\u0026ndash;1.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.502\u003c/p\u003e\n\u003cp\u003e0.645\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCI\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03\u0026ndash;1.29\u003c/p\u003e\n\u003cp\u003e0.90\u0026ndash;1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.016*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e0.636\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00-1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48\u0026ndash;1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeGFR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99-1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAll adjusted regression models include age, sex, eGFR and CCI. Statistically significant p-values are shown in bold, and the level of statistical significance is marked with asterisks (*/**/***).\u003c/p\u003e\n\u003cp\u003eIQR\u0026thinsp;=\u0026thinsp;interquartile range, OR\u0026thinsp;=\u0026thinsp;odds ratio, CI\u0026thinsp;=\u0026thinsp;confidence interval, PIM\u0026thinsp;=\u0026thinsp;potentially inappropriate medication, eGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate, ED\u0026thinsp;=\u0026thinsp;emergency department\u003c/p\u003e\n\u003cp\u003e* = p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** = p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** = p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecurrent ED visits.\u003c/strong\u003e Among patients with ED revisits, the patients who used at least one PIM had more ED revisits within follow-up on average than the patients who did not use PIMs [median (IQR) 2 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) vs. 1 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e), p\u0026thinsp;=\u0026thinsp;0.043]. In the Spearman rank correlative analysis, among the patients with ED revisits (n\u0026thinsp;=\u0026thinsp;165), there was a very weak positive correlation between the number of medications and the number of ED revisits (\u0026rho;\u0026thinsp;=\u0026thinsp;0.16, n\u0026thinsp;=\u0026thinsp;165, p\u0026thinsp;=\u0026thinsp;0.042). Additionally, a weak positive correlation was observed between the number of PIMs and the number of ED revisits (\u0026rho;\u0026thinsp;=\u0026thinsp;0.28, n\u0026thinsp;=\u0026thinsp;165, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar positive correlations were observed in the whole study population between the number of regular medications (\u0026rho;\u0026thinsp;=\u0026thinsp;0.19, n\u0026thinsp;=\u0026thinsp;391, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the number of PIMs (\u0026rho;\u0026thinsp;=\u0026thinsp;0.10, n\u0026thinsp;=\u0026thinsp;386, p\u0026thinsp;=\u0026thinsp;0.043) and the number of ED revisits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePIM criteria.\u003c/strong\u003e In the supplementary analysis, the PIM prevalence according to STOPP/START criteria remained similar to Beers criteria (60% vs 60%) used in the primary analyses. However, PIM prevalence was lower than with Beers, when Meds75\u0026thinsp;+\u0026thinsp;criteria was used (30% vs 60%). The median number and range of PIMs were similar between the three criteria (\u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table\u0026nbsp;2\u003c/strong\u003e presents the unadjusted and adjusted odds ratios for logistic regression models including STOPP/START or Meds+ criteria and their association with primary study outcomes. According to STOPP/START criteria, use of an additional PIM predicted a significantly higher risk of 90-day ED revisit in both the unadjusted [OR 1.24, 95% CI (1.04\u0026ndash;1.49), p\u0026thinsp;=\u0026thinsp;0.018) and the adjusted [OR\u0026thinsp;=\u0026thinsp;1.22, 95% CI (1.01\u0026ndash;1.48), p\u0026thinsp;=\u0026thinsp;0.036) models. Otherwise, PIM use was not associated with significantly lower or higher risks of study outcomes, similarly as with Beers criteria.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOlder adults account for at least one-fourth of all ED visits, and their visit rates are rapidly increasing due to global population ageing (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Consequently, geriatric emergency medicine research has become a key focus area in emergency medicine research (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). To our knowledge, this is the first study to investigate the effects of both polypharmacy and PIM use on short-term outcomes after ED visit in older patients, simultaneously adjusting for the impact of underlying comorbidities. To assess the effects of number of medications and PIM use beyond the currently used numerical thresholds, we used both binomial and ordinal study variables.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrevalence of polypharmacy and PIM use.\u003c/b\u003e Our findings confirm that polypharmacy is highly prevalent among older ED patients: 80% of the included patients used five or more medications regularly, while 30% of the patients used 10 or more. This aligns with global and national estimates among community-dwelling older adults, additionally reflecting a rapid growth in polypharmacy prevalence (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The prevalence of PIM use in our study was consistent with both global and national estimates among community-dwelling older adults, suggesting a prevalence of approximately 37% (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Some studies have reported a lower prevalence of polypharmacy in ED setting: Van Dam et al reported prevalence of 43% for polypharmacy and 18% for excessive polypharmacy, whereas Ruiz Ramos et al reported prevalences of 41% and 22%, respectively (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These observed differences could be linked to variance in the study populations, local prescribing practice or data collection schemes.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolypharmacy and study outcomes.\u003c/b\u003e Unexpectedly, our study found that polypharmacy did not predict a higher risk of mortality or hospital admissions from the ED when confounding factors including morbidity were considered. In fact, polypharmacy was associated with a lower risk of mortality, and the result was confirmed in two adjusted regression models using binomial and ordinal variables as determinants of polypharmacy. It is possible that the polypharmacy patients in this study represented a selected healthier group of patients for whom the use of multiple medications is considered safe. However, the lower mortality risk associated with polypharmacy may also reflect the prognosis-improving effects of \u0026ldquo;appropriate polypharmacy. In recent years, multiple studies have suggested that polypharmacy should not inherently be regarded as harmful and medication reviews should focus on medication appropriateness instead of the absolute number of medications (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, we found that excessive polypharmacy and increasing number of medications predicted a higher risk of ED revisits. The potential negative effects of excessive medication in outpatient setting have been recognized in previous studies, suggesting that excessive polypharmacy is associated with drug interactions and unplanned hospitalizations among older people (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In ED setting, a study by Salvi et al. found that the use of \u0026ge;\u0026thinsp;10 medications was associated with higher risk of early and late ED revisits, late hospital admissions and death in older patients (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Aligning with these findings, our study results indicate that excessive polypharmacy is a stronger risk factor than polypharmacy for predicting negative outcomes such as ED revisits among older ED patients.\u003c/p\u003e \u003cp\u003eIn contrast to our findings, Van Dam et al. found that polypharmacy was an independent predictor for 90-day mortality but not for ED revisits (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These differences may be caused by the earlier mentioned distinctions in polypharmacy prevalence, which may result from the significantly different study populations: van Dam et al. included patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years who gave written consent, whereas our study included patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years and patient consent was not required. Lastly, van Dam et al. adjusted for ISAR-HP \u0026ndash; a frailty screening tool originally developed to detect functional decline in hospitalized older patients \u0026ndash; to correct for frailty in the analyses (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Regarding the previously reported poor ability of ISAR-HP to detect frailty, and the significant decline resulting from adjusting for ISAR-HP in the predictive value of polypharmacy for ED revisits in their study, including ISAR-HP in the analyses may have confounded the results on ED revisits (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). On the other hand, we acknowledge that we were unable to consider frailty due to the retrospective nature of our study.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePIM use and study outcomes.\u003c/b\u003e Our findings regarding the associations between PIM use and short-term adverse outcomes are consistent with previous studies assessing long-term outcomes, as they have found no association between PIM use, mortality, or ED revisits (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Considering that previous literature has reported a large variance in PIM prevalence among different PIM criteria, we performed supplementary analyses using the European STOPP/START and the Finnish Meds75\u0026thinsp;+\u0026thinsp;criteria in addition to Beers criteria (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In these analyses, in contrast to Beers or Meds75+, each additional STOPP/START PIM was associated with a significantly higher risk of ED revisits. Otherwise, there were no significant associations between PIM use and study outcomes, regardless of the criteria.\u003c/p\u003e \u003cp\u003eDespite being beneficial in outpatient setting, assessing appropriateness of individual medications of an older patient with polypharmacy remains challenging in ED setting, and missing or inaccurate medication lists further complicate the matter (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Although pharmacist-led medication review/reconciliation has shown some benefits also in the context of acute care (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), the approach to reduce PIM use in ED setting has not resulted in better clinical outcomes (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the light of previous literature and our study results, we suggest that PIMs most likely play a role in the ED revisits of older ED patients, but identifying individual PIMs and interactions with patient characteristics may be of greater importance as identifying PIMs as a group in ED setting. Comprehensive medication reviews are beneficial but should be performed non-urgently for high-risk patients, for example in home care, where more time and information are available. This kind of thorough outpatient medication review would additionally prevent unnecessary ED visits for frail older adults (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). However, the standardized methods to identify these high-risk patients in ED settings remain unclear.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFrequent ED visits.\u003c/b\u003e According to Spearman correlative analysis, polypharmacy and PIM use had a positive correlation with the number of ED revisits, suggesting that older patients with drug-related problems may be at a higher risk of frequent ED use. However, the statistical power of the analysis remained low, and therefore these results should be confirmed in larger samples. It is, however, understandable that DRPs lead to repeat ED visits especially if the underlying medication-related issues are not identified and managed at the ED. Even though medication-related problems may not be resolved at the ED, identifying them or the patients requiring medication review would help referring patients to appropriate services to reduce future health care needs.\u003c/p\u003e \u003cp\u003e\u003cb\u003eCurrent frailty screening and risk stratification tools.\u003c/b\u003e As mentioned earlier, international geriatric emergency medicine guidelines recommend screening for polypharmacy and PIM use in the EDs (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, polypharmacy screening is included in several risk and frailty screening tools designed for ED setting (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, since the numerical threshold for defining polypharmacy is \u0026ge;\u0026thinsp;3 medications in the ISAR tool and \u0026ge;\u0026thinsp;5 in the TRST, including polypharmacy with these thresholds as an item may even reduce the positive predictive value of these tools, which have recently shown poor prognostic accuracy in identifying frailty or high-risk older patients (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Considering these findings and the high prevalence of the use of five or more regular medications among older ED patients, we suggest the numerical threshold for polypharmacy screening should be updated to higher such as 10 medications or over, or more emphasis should be paid on certain medications or drug classes associated with the highest risks (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplementations for the future.\u003c/b\u003e Our results indicate that underlying morbidity is a strong determinant of adverse outcomes among older ED patients. However, the association between the use of five or more medications and these outcomes is weak, aligning with the increasing evidence that polypharmacy is often inevitable to reach therapeutic equilibrium in chronic illness. However, as we found that use of ten or more medications is associated with ED revisits, we propose that polypharmacy screening should be performed in ED setting but with novel approaches. However, more prospective studies are needed to not only determine the optimal polypharmacy thresholds but also to confirm the benefits of medication screening in the ED.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eWe incorporated a large, high-quality dataset of older ED patients. To limit the impact and evaluate the accuracy of current numerical thresholds, we evaluated medication use with both binomial and ordinal variables. Additionally, we defined CCI for each patient to consider the effect of underlying comorbidities to study outcomes. Furthermore, to evaluate the differences between the most common PIM criteria, separate supplementary analyses for each criterion were performed.\u003c/p\u003e \u003cp\u003eHowever, limitations of this study must be acknowledged. Although we adjusted for multiple patient-specific confounding factors in the analysis, the impact of underlying comorbidities could not be entirely removed. As no frailty measurement was carried out at the ED, we could not adjust for frailty. Furthermore, due to retrospective study design and limited information of the indications for medication use, we could not recognize DRPs and ADEs, medication timeliness or patient adherence. Therefore, pro re nata (PRN) medications were not included in the data analysis. Medication use after the ED visit could not be measured, which should also be considered as medication administration practices could have changed during the follow-up. Lastly, the study population did not include older adults living in nursing homes or long-term care facilities \u0026ndash; thus, the study results should be considered only in the population of community-dwelling older adults.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePolypharmacy and PIM use are common among older ED patients. With the threshold of \u0026ge;\u0026thinsp;5, polypharmacy does not predict higher risk of study outcomes but predicts a lower mortality risk. Excessive polypharmacy and each addition in the number of medications predicts a higher risk of ED revisits. PIM use does not predict adverse outcomes. Current approaches for polypharmacy and PIM screening in ED setting should be updated to identify high-risk older patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican College of Emergency Physicians\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadverse drug event\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edrug-related problem\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eemergency department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eemergency medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eemergency medical services\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEuGMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Geriatric Medicine Society\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEUSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Society of Emergency Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHUH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHelsinki University Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e10th revision of International Statistical Classification of Diseases and Related Health Problems\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIdentification of Seniors At Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epotentially inappropriate medication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epro re nata\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTOPP/START\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScreening Tool of Older Persons' Prescriptions / Screening Tool to Alert to Right Treatment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriage Risk Screening Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eThe study design was approved by the Helsinki University Hospital (HUH) District (Ref. no. \u0026sect;\u0026nbsp;85 HUS/223/2023). All procedures performed in this study were in accordance with the Declaration of Helsinki. Due to the retrospective and observational nature of the study, patient consent or approval from the HUH Regional Committee on Medical Research Ethics was not required.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the public government research fund of Helsinki University Hospital Emergency Medicine and Services.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRH gathered the medication lists of included patients and combined it with the previously collected clinical data, took part in the conceptualization, performed the statistical analysis, created the figures and tables and prepared the initial and final version of the manuscript. MH took part in planning and conceptualization of the study and assisted with statistical analysis and demonstration of the results. ES gathered the initial clinical data of included patients and assisted in preparing the manuscript. EJ, MC and JK took part in planning, conceptualization and project coordination and reviewing the manuscript. KK took part in planning the project and consulted the pharmacological aspects of the study. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge Helsinki University Hospital Emergency Medicine and Services, Faculty of Medicine in University of Helsinki and Emergency Medical Services (EMS) of Helsinki University Hospital area for the effortless collaboration regarding this research project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNymoen LD, Bj\u0026ouml;rk M, Flateb\u0026oslash; TE, Nilsen M, God\u0026oslash; A, \u0026Oslash;ie E, et al. Drug-related emergency department visits: prevalence and risk factors. Intern Emerg Med. 2022;17(5):1453\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11739-022-02935-9\u003c/span\u003e\u003cspan address=\"10.1007/s11739-022-02935-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 35129789.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHohl CM, Zed PJ, Brubacher JR, Abu-Laban RB, Loewen PS, Purssell RA. Do Emergency Physicians Attribute Drug-Related Emergency Department Visits to Medication-Related Problems? Ann Emerg Med. 2010;55(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.annemergmed.2009.10.008\u003c/span\u003e\u003cspan address=\"10.1016/j.annemergmed.2009.10.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoulet L, Ballereau F, Hardouin JB, Chiffoleau A, Potel G, Asseray N. Adverse drug event nonrecognition in emergency departments: An exploratory study on factors related to patients and drugs. J Emerg Med. 2014;46(6):857\u0026ndash;64. PubMed PMID: 24565882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoulet L, Ballereau F, Hardouin JB, Chiffoleau A, Moret L, Potel G, et al. Assessment of adverse drug event recognition by emergency physicians in a French teaching hospital. Emerg Med J. 2013;30(1):63\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/emermed-2011-200482\u003c/span\u003e\u003cspan address=\"10.1136/emermed-2011-200482\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 22366041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNymoen LD, Bj\u0026ouml;rk M, Flateb\u0026oslash; TE, Nilsen M, God\u0026oslash; A, \u0026Oslash;ie E, et al. Drug-related emergency department visits: prevalence and risk factors. 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Eur Geriatr Med. 2023;14(4):823\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s41999-023-00799-6\u003c/span\u003e\u003cspan address=\"10.1007/s41999-023-00799-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Emergency medicine, geriatrics, geriatric emergency medicine, polypharmacy, potentially inappropriate medication, drug-related problems, drug-related emergency department visit, frailty screening tool","lastPublishedDoi":"10.21203/rs.3.rs-9326583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9326583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003ePolypharmacy and potentially inappropriate medication (PIM) use are well-known risk factors for drug-related problems among older adults, but their association with clinical outcomes of older emergency department (ED) patients remains unclear. This study investigated the predictive value of polypharmacy and PIM use in older ED patient population.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eWe included 392 ED patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years. Polypharmacy and PIM (according to 2019 Beers criteria) use were determined with binomial variables (polypharmacy\u0026thinsp;\u0026ge;\u0026thinsp;5 regular medications, excessive polypharmacy\u0026thinsp;\u0026ge;\u0026thinsp;10 medications, PIM use\u0026thinsp;\u0026ge;\u0026thinsp;1 PIMs) and ordinal variables (the number of regular medications and PIMs). Study outcomes included mortality, hospital admission following index ED presentation, and ED revisit within 90 days. Statistically, we used logistic regression model adjusted for age, sex, renal function and the Charlson Comorbidity Index (CCI).\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003ePolypharmacy was present in 80% and excessive polypharmacy in 30% of patients. As a binomial variable, polypharmacy did not predict any outcomes, but excessive polypharmacy [adjusted OR 1.35 (95% CI 1.12\u0026ndash;4.93), p\u0026thinsp;=\u0026thinsp;0.024] and increasing number of medications [1.09 (1.03\u0026ndash;1.16), p\u0026thinsp;=\u0026thinsp;0.014] predicted a higher risk of ED revisits. Polypharmacy [OR 0.17 (0.06\u0026ndash;0.45), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], excessive polypharmacy [0.21 (0.06\u0026ndash;0.70), p\u0026thinsp;=\u0026thinsp;0.01] and increasing number of medications [0.83 (0.72\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.005] were associated with lower mortality risk.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003ePolypharmacy or PIM use are not associated with higher mortality or hospital admission risk. Polypharmacy predicts a higher risk of ED revisits, but only with the threshold of \u0026ge;\u0026thinsp;10 medications. Screening polypharmacy with novel numerical thresholds could be useful in detecting older ED patients at high risk of ED revisits.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Polypharmacy and Inappropriate Medication Use in Older Emergency Department Patients: A Retrospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 15:51:12","doi":"10.21203/rs.3.rs-9326583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T23:56:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T21:12:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226360327392111661718454741531002839641","date":"2026-05-11T12:04:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T13:07:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85047391042512692135690946657696340910","date":"2026-04-22T15:11:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185931571649388050071677616212131337297","date":"2026-04-22T14:42:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T09:58:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T09:40:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T11:17:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T11:16:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2026-04-05T13:34:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88322849-124e-4b32-912b-f28974ad5d40","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T23:56:53+00:00","index":87,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T21:12:51+00:00","index":86,"fulltext":""},{"type":"reviewerAgreed","content":"226360327392111661718454741531002839641","date":"2026-05-11T12:04:34+00:00","index":85,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:51:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 15:51:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9326583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9326583","identity":"rs-9326583","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}